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| Content Provider | Springer Nature Link |
|---|---|
| Author | Li, Hong Gatsonis, Constantine |
| Copyright Year | 2012 |
| Abstract | Surveillance to detect cancer recurrence is an important part of care for cancer survivors. In this paper we discuss the design of optimal strategies for early detection of disease recurrence based on each patient’s distinct biomarker trajectory and periodically updated risk estimated in the setting of a prospective cohort study. We adopt a latent class joint model which considers a longitudinal biomarker process and an event process jointly, to address heterogeneity of patients and disease, to discover distinct biomarker trajectory patterns, to classify patients into different risk groups, and to predict the risk of disease recurrence. The model is used to develop a monitoring strategy that dynamically modifies the monitoring intervals according to patients’ current risk derived from periodically updated biomarker measurements and other indicators of disease spread. The optimal biomarker assessment time is derived using a utility function. We develop an algorithm to apply the proposed strategy to monitoring of new patients after initial treatment. We illustrate the models and the derivation of the optimal strategy using simulated data from monitoring prostate cancer recurrence over a 5-year period. |
| Starting Page | 1565 |
| Ending Page | 1582 |
| Page Count | 18 |
| File Format | |
| ISSN | 16747283 |
| Journal | Science in China Series A: Mathematics |
| Volume Number | 55 |
| Issue Number | 8 |
| e-ISSN | 18691862 |
| Language | English |
| Publisher | SP Science China Press |
| Publisher Date | 2012-07-18 |
| Publisher Place | Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | biomarker trajectory cancer recurrence surveillance latent class model optimal strategy timedependent hazard Applications of Mathematics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Mathematics |
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